DESCRIPTION (Applicant's Description) Categorical data having both ordinal and nominal occur frequently in biomedical studies. Such categorical data are referred to as mixed categorical data in this proposal, For example, a response to a question may be categorized as 'Strongly Approve', 'Approve', 'Neutral', 'Disapprove', 'Strongly Disapprove', and 'Don't Know'. This example consists of five ordered categories and one nominal category. Most analyses performed with mixed categorical data simply discard the information on such nominal category or discard the order information. Thus the information is lost in either of these approaches. In this proposal we suggest a method of analyzing mixed categorical data in 2xC contingency tables. The proposed method maximizes and minimizes the square of the Pearson correlation coefficient between row and column variables over all order preserving, scores for the ordered categories and overall scores to the nominal categories. The maximum can be used to test hypothesis of independence or homogeneity. The proposed method brings the correspondence analysis and standard statistical methods together. Maximization and minimization together allows an investigator to do a preliminary examination of the data set to see whether or not the statistical results are sensitive to the choice of scores. We propose to extend the method where the scores for the ordered part of the categories are known or given. In this situation the optimization will be carried out over all scores for the nominal categories keeping the scores to the ordered categories fixed. To achieve these goals, least square method including isotonic regression will be used, Simulations will be used to compare the existing practice with the proposed method. Computer programs will be written to implement the algorithms developed. To exemplify the methods, previously published real data sets will be analyzed.